We describe our participation in the Knowledge Base Population (KBP) English slot filling track of TAC. Our system is based on a distant supervision approach, where training instances are created by heuristically matching DBpedia relation tuples with sentences. Our official submission, which uses web snippets returned by search engines to collect positive instances, results in a performance comparable to the median. Based on an intial analysis we find a number of shortcomings with our official runs. After the TAC evaluation, we submit an unofficial run which matches relation tuples with sentences appearing in Wikipedia pages. Our proposed solutions implemented in our unofficial run did not further improve performance.
[1]
Christopher D. Manning,et al.
Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling
,
2005,
ACL.
[2]
Daniel Jurafsky,et al.
Distant supervision for relation extraction without labeled data
,
2009,
ACL.
[3]
Thorsten Joachims,et al.
Making large-scale support vector machine learning practical
,
1999
.
[4]
Valentin I. Spitkovsky,et al.
A Simple Distant Supervision Approach for the TAC-KBP Slot Filling Task
,
2010,
TAC.
[5]
W. Bruce Croft,et al.
Indri: A language-model based search engine for complex queries1
,
2005
.